纯C++版的Faster-Rcnn（通过caffe自定义RPN层实现）

这里介绍的是通过添加自定义层（RPN层）代替python层，实现c++版的faster-rcnn，因为去掉python了，所以部署时不会因为牵扯到python库等其它的莫名其妙的错误，使用起来就跟单纯的caffe一样，更简单方便。核心代码，借鉴的是这篇博客，这里的话，我们不扣具体的代码细节（比如rpn层是怎么产出候选框啊，非极大值抑制是具体怎么实现的等等），有兴趣的可以自己查下资料，所以主要是走一个步骤，从而完成c++版faster-rcnn的配置。

http://blog.csdn.net/u010327085/article/details/54342070

步入正题，步骤和上面那篇博客大致一样，但它有一些细节地方直接忽略了，代码也有几处小bug，所以我把具体的流程给说下。

（1）添加自定义层 rpn_layer.hpp 把它放在 caffe/include/caffe/layers/ 目录下

[cpp] view plaincopy

#ifndef CAFFE_RPN_LAYER_HPP_
#define CAFFE_RPN_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
//#include"opencv2/opencv.hpp"
#define mymax(a,b) ((a)>(b))?(a):(b)
#define mymin(a,b) ((a)>(b))?(b):(a)
namespace caffe {
/**
* @brief implement RPN layer for faster rcnn
*/
template <typename Dtype>
class RPNLayer : public Layer<Dtype> {
public:
explicit RPNLayer(const LayerParameter& param)
: Layer<Dtype>(param) {
m_score_.reset(new Blob<Dtype>());
m_box_.reset(new Blob<Dtype>());
local_anchors_.reset(new Blob<Dtype>());
}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top){}
virtual inline const char* type() const { return "RPN"; }
struct abox{
Dtype batch_ind;
Dtype x1;
Dtype y1;
Dtype x2;
Dtype y2;
Dtype score;
bool operator <(const abox&tmp) const{
return score < tmp.score;
}
};
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
//virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
//const vector<Blob<Dtype>*>& top);
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom){};
int feat_stride_;
int base_size_;
int min_size_;
int pre_nms_topN_;
int post_nms_topN_;
float nms_thresh_;
vector<int> anchor_scales_;
vector<float> ratios_;
vector<vector<float> > gen_anchors_;
int *anchors_;
int anchors_nums_;
int src_height_;
int src_width_;
float src_scale_;
int map_width_;
int map_height_;
shared_ptr<Blob<Dtype> > m_score_;
shared_ptr<Blob<Dtype> > m_box_;
shared_ptr<Blob<Dtype> >local_anchors_;
void generate_anchors();
vector<vector<float> > ratio_enum(vector<float>);
vector<float> whctrs(vector<float>);
vector<float> mkanchor(float w,float h,float x_ctr,float y_ctr);
vector<vector<float> > scale_enum(vector<float>);
//cv::Mat proposal_local_anchor(int width, int height);
void proposal_local_anchor();
void bbox_tranform_inv();
cv::Mat bbox_tranform_inv(cv::Mat local_anchors, cv::Mat boxs_delta);
void nms(std::vector<abox> &input_boxes, float nms_thresh);
void filter_boxs(cv::Mat& pre_box, cv::Mat& score, vector<abox>& aboxes);
void filter_boxs(vector<abox>& aboxes);
};
} // namespace caffe
#endif // CAFFE_RPN_LAYER_HPP_

然后是源文件 rpn_layer.cpp 放在 caffe/src/caffe/layers/ 目录下

[cpp] view plaincopy

#include <algorithm>
#include <vector>
#include "caffe/layers/rpn_layer.hpp"
#include "caffe/util/math_functions.hpp"
#include <opencv2/opencv.hpp>
int debug = 0;
int tmp[9][4] = {
{ -83, -39, 100, 56 },
{ -175, -87, 192, 104 },
{ -359, -183, 376, 200 },
{ -55, -55, 72, 72 },
{ -119, -119, 136, 136 },
{ -247, -247, 264, 264 },
{ -35, -79, 52, 96 },
{ -79, -167, 96, 184 },
{ -167, -343, 184, 360 }
};
namespace caffe {
template <typename Dtype>
void RPNLayer<Dtype>::LayerSetUp(
const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
anchor_scales_.clear();
ratios_.clear();
feat_stride_ = this->layer_param_.rpn_param().feat_stride();
base_size_ = this->layer_param_.rpn_param().basesize();
min_size_ = this->layer_param_.rpn_param().boxminsize();
pre_nms_topN_ = this->layer_param_.rpn_param().per_nms_topn();
post_nms_topN_ = this->layer_param_.rpn_param().post_nms_topn();
nms_thresh_ = this->layer_param_.rpn_param().nms_thresh();
int scales_num = this->layer_param_.rpn_param().scale_size();
for (int i = 0; i < scales_num; ++i)
{
anchor_scales_.push_back(this->layer_param_.rpn_param().scale(i));
}
int ratios_num = this->layer_param_.rpn_param().ratio_size();
for (int i = 0; i < ratios_num; ++i)
{
ratios_.push_back(this->layer_param_.rpn_param().ratio(i));
}
//anchors_nums_ = 9;
//anchors_ = new int[anchors_nums_ * 4];
//memcpy(anchors_, tmp, 9 * 4 * sizeof(int));
generate_anchors();
anchors_nums_ = gen_anchors_.size();
anchors_ = new int[anchors_nums_ * 4];
for (int i = 0; i<gen_anchors_.size(); ++i)
{
for (int j = 0; j<gen_anchors_[i].size(); ++j)
{
anchors_[i*4+j] = gen_anchors_[i][j];
}
}
top[0]->Reshape(1, 5, 1, 1);
if (top.size() > 1)
{
top[1]->Reshape(1, 1, 1, 1);
}
}
template <typename Dtype>
void RPNLayer<Dtype>::generate_anchors(){
//generate base anchor
vector<float> base_anchor;
base_anchor.push_back(0);
base_anchor.push_back(0);
base_anchor.push_back(base_size_ - 1);
base_anchor.push_back(base_size_ - 1);
//enum ratio anchors
vector<vector<float> >ratio_anchors = ratio_enum(base_anchor);
for (int i = 0; i < ratio_anchors.size(); ++i)
{
vector<vector<float> > tmp = scale_enum(ratio_anchors[i]);
gen_anchors_.insert(gen_anchors_.end(), tmp.begin(), tmp.end());
}
}
template <typename Dtype>
vector<vector<float> > RPNLayer<Dtype>::scale_enum(vector<float> anchor){
vector<vector<float> > result;
vector<float> reform_anchor = whctrs(anchor);
float x_ctr = reform_anchor[2];
float y_ctr = reform_anchor[3];
float w = reform_anchor[0];
float h = reform_anchor[1];
for (int i = 0; i < anchor_scales_.size(); ++i)
{
float ws = w * anchor_scales_[i];
float hs = h * anchor_scales_[i];
vector<float> tmp = mkanchor(ws, hs, x_ctr, y_ctr);
result.push_back(tmp);
}
return result;
}
template <typename Dtype>
vector<vector<float> > RPNLayer<Dtype>::ratio_enum(vector<float> anchor){
vector<vector<float> > result;
vector<float> reform_anchor = whctrs(anchor);
float x_ctr = reform_anchor[2];
float y_ctr = reform_anchor[3];
float size = reform_anchor[0] * reform_anchor[1];
for (int i = 0; i < ratios_.size(); ++i)
{
float size_ratios = size / ratios_[i];
float ws = round(sqrt(size_ratios));
float hs = round(ws*ratios_[i]);
vector<float> tmp = mkanchor(ws, hs, x_ctr, y_ctr);
result.push_back(tmp);
}
return result;
}
template <typename Dtype>
vector<float> RPNLayer<Dtype>::mkanchor(float w, float h, float x_ctr, float y_ctr){
vector<float> tmp;
tmp.push_back(x_ctr - 0.5*(w - 1));
tmp.push_back(y_ctr - 0.5*(h - 1));
tmp.push_back(x_ctr + 0.5*(w - 1));
tmp.push_back(y_ctr + 0.5*(h - 1));
return tmp;
}
template <typename Dtype>
vector<float> RPNLayer<Dtype>::whctrs(vector<float> anchor){
vector<float> result;
result.push_back(anchor[2] - anchor[0] + 1); //w
result.push_back(anchor[3] - anchor[1] + 1); //h
result.push_back((anchor[2] + anchor[0]) / 2); //ctrx
result.push_back((anchor[3] + anchor[1]) / 2); //ctry
return result;
}
/*template <typename Dtype>
cv::Mat RPNLayer<Dtype>::proposal_local_anchor(int width, int height)
{
Blob<float> shift;
cv::Mat shitf_x(height, width, CV_32SC1);
cv::Mat shitf_y(height, width, CV_32SC1);
for (size_t i = 0; i < width; i++)
{
for (size_t j = 0; j < height; j++)
{
shitf_x.at<int>(j, i) = i * feat_stride_;
shitf_y.at<int>(j, i) = j * feat_stride_;
}
}
shift.Reshape(anchors_nums_, width*height, 4, 1);
float *p = shift.mutable_cpu_diff(), *a = shift.mutable_cpu_data();
for (int i = 0; i < height*width; i++)
{
for (int j = 0; j < anchors_nums_; j++)
{
size_t num = i * 4 + j * 4 * height*width;
p[num + 0] = -shitf_x.at<int>(i / shitf_x.cols, i % shitf_x.cols);
p[num + 2] = -shitf_x.at<int>(i / shitf_x.cols, i % shitf_x.cols);
p[num + 1] = -shitf_y.at<int>(i / shitf_y.cols, i % shitf_y.cols);
p[num + 3] = -shitf_y.at<int>(i / shitf_y.cols, i % shitf_y.cols);
a[num + 0] = anchors_[j * 4 + 0];
a[num + 1] = anchors_[j * 4 + 1];
a[num + 2] = anchors_[j * 4 + 2];
a[num + 3] = anchors_[j * 4 + 3];
}
}
shift.Update();
cv::Mat loacl_anchors(anchors_nums_ * height*width, 4, CV_32FC1);
size_t num = 0;
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
for (int c = 0; c < anchors_nums_; ++c)
{
for (int k = 0; k < 4; ++k)
{
loacl_anchors.at<float>((i*width + j)*anchors_nums_+c, k)= shift.data_at(c, i*width + j, k, 0);
}
}
}
}
return loacl_anchors;
}*/
template <typename Dtype>
void RPNLayer<Dtype>::proposal_local_anchor(){
int length = mymax(map_width_, map_height_);
int step = map_width_*map_height_;
int *map_m = new int[length];
for (int i = 0; i < length; ++i)
{
map_m[i] = i*feat_stride_;
}
Dtype *shift_x = new Dtype[step];
Dtype *shift_y = new Dtype[step];
for (int i = 0; i < map_height_; ++i)
{
for (int j = 0; j < map_width_; ++j)
{
shift_x[i*map_width_ + j] = map_m[j];
shift_y[i*map_width_ + j] = map_m[i];
}
}
local_anchors_->Reshape(1, anchors_nums_ * 4, map_height_, map_width_);
Dtype *a = local_anchors_->mutable_cpu_data();
for (int i = 0; i < anchors_nums_; ++i)
{
caffe_set(step, Dtype(anchors_[i * 4 + 0]), a + (i * 4 + 0) *step);
caffe_set(step, Dtype(anchors_[i * 4 + 1]), a + (i * 4 + 1) *step);
caffe_set(step, Dtype(anchors_[i * 4 + 2]), a + (i * 4 + 2) *step);
caffe_set(step, Dtype(anchors_[i * 4 + 3]), a + (i * 4 + 3) *step);
caffe_axpy(step, Dtype(1), shift_x, a + (i * 4 + 0)*step);
caffe_axpy(step, Dtype(1), shift_x, a + (i * 4 + 2)*step);
caffe_axpy(step, Dtype(1), shift_y, a + (i * 4 + 1)*step);
caffe_axpy(step, Dtype(1), shift_y, a + (i * 4 + 3)*step);
}
}
template<typename Dtype>
void RPNLayer<Dtype>::filter_boxs(cv::Mat& pre_box, cv::Mat& score, vector<abox>& aboxes)
{
float localMinSize=min_size_*src_scale_;
aboxes.clear();
for (int i = 0; i < pre_box.rows; i++)
{
int widths = pre_box.at<float>(i, 2) - pre_box.at<float>(i, 0) + 1;
int heights = pre_box.at<float>(i, 3) - pre_box.at<float>(i, 1) + 1;
if (widths >= localMinSize || heights >= localMinSize)
{
abox tmp;
tmp.x1 = pre_box.at<float>(i, 0);
tmp.y1 = pre_box.at<float>(i, 1);
tmp.x2 = pre_box.at<float>(i, 2);
tmp.y2 = pre_box.at<float>(i, 3);
tmp.score = score.at<float>(i, 0);
aboxes.push_back(tmp);
}
}
}
template<typename Dtype>
void RPNLayer<Dtype>::filter_boxs(vector<abox>& aboxes)
{
float localMinSize = min_size_*src_scale_;
aboxes.clear();
int map_width = m_box_->width();
int map_height = m_box_->height();
int map_channel = m_box_->channels();
const Dtype *box = m_box_->cpu_data();
const Dtype *score = m_score_->cpu_data();
int step = 4 * map_height*map_width;
int one_step = map_height*map_width;
int offset_w, offset_h, offset_x, offset_y, offset_s;
for (int h = 0; h < map_height; ++h)
{
for (int w = 0; w < map_width; ++w)
{
offset_x = h*map_width + w;
offset_y = offset_x + one_step;
offset_w = offset_y + one_step;
offset_h = offset_w + one_step;
offset_s = one_step*anchors_nums_+h*map_width + w;
for (int c = 0; c < map_channel / 4; ++c)
{
Dtype width = box[offset_w], height = box[offset_h];
if (width < localMinSize || height < localMinSize)
{
}
else
{
abox tmp;
tmp.batch_ind = 0;
tmp.x1 = box[offset_x] - 0.5*width;
tmp.y1 = box[offset_y] - 0.5*height;
tmp.x2 = box[offset_x] + 0.5*width;
tmp.y2 = box[offset_y] + 0.5*height;
tmp.x1 = mymin(mymax(tmp.x1, 0), src_width_);
tmp.y1 = mymin(mymax(tmp.y1, 0), src_height_);
tmp.x2 = mymin(mymax(tmp.x2, 0), src_width_);
tmp.y2 = mymin(mymax(tmp.y2, 0), src_height_);
tmp.score = score[offset_s];
aboxes.push_back(tmp);
}
offset_x += step;
offset_y += step;
offset_w += step;
offset_h += step;
offset_s += one_step;
}
}
}
}
template<typename Dtype>
void RPNLayer<Dtype>::bbox_tranform_inv(){
int channel = m_box_->channels();
int height = m_box_->height();
int width = m_box_->width();
int step = height*width;
Dtype * a = m_box_->mutable_cpu_data();
Dtype * b = local_anchors_->mutable_cpu_data();
for (int i = 0; i < channel / 4; ++i)
{
caffe_axpy(2*step, Dtype(-1), b + (i * 4 + 0)*step, b + (i * 4 + 2)*step);
caffe_add_scalar(2 * step, Dtype(1), b + (i * 4 + 2)*step);
caffe_axpy(2*step, Dtype(0.5), b + (i * 4 + 2)*step, b + (i * 4 + 0)*step);
caffe_mul(2 * step, b + (i * 4 + 2)*step, a + (i * 4 + 0)*step, a + (i * 4 + 0)*step);
caffe_add(2 * step, b + (i * 4 + 0)*step, a + (i * 4 + 0)*step, a + (i * 4 + 0)*step);
caffe_exp(2*step, a + (i * 4 + 2)*step, a + (i * 4 + 2)*step);
caffe_mul(2 * step, b + (i * 4 + 2)*step, a + (i * 4 + 2)*step, a + (i * 4 + 2)*step);
}
}
template<typename Dtype>
void RPNLayer<Dtype>::nms(std::vector<abox> &input_boxes, float nms_thresh){
std::vector<float>vArea(input_boxes.size());
for (int i = 0; i < input_boxes.size(); ++i)
{
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1)
* (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 1);
}
for (int i = 0; i < input_boxes.size(); ++i)
{
for (int j = i + 1; j < input_boxes.size();)
{
float xx1 = std::max(input_boxes[i].x1, input_boxes[j].x1);
float yy1 = std::max(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = std::min(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = std::min(input_boxes[i].y2, input_boxes[j].y2);
float w = std::max(float(0), xx2 - xx1 + 1);
float h = std::max(float(0), yy2 - yy1 + 1);
float inter = w * h;
float ovr = inter / (vArea[i] + vArea[j] - inter);
if (ovr >= nms_thresh)
{
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
}
else
{
j++;
}
}
}
}
template <typename Dtype>
void RPNLayer<Dtype>::Forward_cpu(
const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
map_width_ = bottom[1]->width();
map_height_ = bottom[1]->height();
//int channels = bottom[1]->channels();
//get boxs_delta,向右。
m_box_->CopyFrom(*(bottom[1]), false, true);
/*cv::Mat boxs_delta(height*width*anchors_nums_, 4, CV_32FC1);
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
for (int k = 0; k < anchors_nums_; ++k)
{
for (int c = 0; c < 4; ++c)
{
boxs_delta.at<float>((i*width + j)*anchors_nums_ + k, c) = bottom[1]->data_at(0, k*4 + c, i, j);
}
}
}
}*/
//get sores 向右，前面anchors_nums_个位bg的得分，后面anchors_nums_为fg得分，我们需要的是后面的。
m_score_->CopyFrom(*(bottom[0]),false,true);
/*cv::Mat scores(height*width*anchors_nums_, 1, CV_32FC1);
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
for (int k = 0; k < anchors_nums_; ++k)
{
scores.at<float>((i*width + j)*anchors_nums_+k, 0) = bottom[0]->data_at(0, k + anchors_nums_, i, j);
}
}
}*/
//get im_info
src_height_ = bottom[2]->data_at(0, 0,0,0);
src_width_ = bottom[2]->data_at(0, 1,0,0);
src_scale_ = bottom[2]->data_at(0, 2, 0, 0);
//gen local anchors 向右
proposal_local_anchor();
//cv::Mat local_anchors = proposal_local_anchor(width, height);
//Convert anchors into proposals via bbox transformations
bbox_tranform_inv();
/*for (int i = 0; i < pre_box.rows; ++i)
{
if (pre_box.at<float>(i, 0) < 0) pre_box.at<float>(i, 0) = 0;
if (pre_box.at<float>(i, 0) > (src_width_ - 1)) pre_box.at<float>(i, 0) = src_width_ - 1;
if (pre_box.at<float>(i, 2) < 0) pre_box.at<float>(i, 2) = 0;
if (pre_box.at<float>(i, 2) > (src_width_ - 1)) pre_box.at<float>(i, 2) = src_width_ - 1;
if (pre_box.at<float>(i, 1) < 0) pre_box.at<float>(i, 1) = 0;
if (pre_box.at<float>(i, 1) > (src_height_ - 1)) pre_box.at<float>(i, 1) = src_height_ - 1;
if (pre_box.at<float>(i, 3) < 0) pre_box.at<float>(i, 3) = 0;
if (pre_box.at<float>(i, 3) > (src_height_ - 1)) pre_box.at<float>(i, 3) = src_height_ - 1;
}*/
vector<abox>aboxes;
filter_boxs(aboxes);
//clock_t start, end;
//start = clock();
std::sort(aboxes.rbegin(), aboxes.rend()); //降序
if (pre_nms_topN_ > 0)
{
int tmp = mymin(pre_nms_topN_, aboxes.size());
aboxes.erase(aboxes.begin() + tmp, aboxes.end());
}
nms(aboxes,nms_thresh_);
//end = clock();
//std::cout << "sort nms:" << (double)(end - start) / CLOCKS_PER_SEC << std::endl;
if (post_nms_topN_ > 0)
{
int tmp = mymin(post_nms_topN_, aboxes.size());
aboxes.erase(aboxes.begin() + tmp, aboxes.end());
}
top[0]->Reshape(aboxes.size(),5,1,1);
Dtype *top0 = top[0]->mutable_cpu_data();
for (int i = 0; i < aboxes.size(); ++i)
{
//caffe_copy(aboxes.size() * 5, (Dtype*)aboxes.data(), top0);
top0[0] = aboxes[i].batch_ind;
top0[1] = aboxes[i].x1;
top0[2] = aboxes[i].y1;
top0[3] = aboxes[i].x2;
top0[4] = aboxes[i].y2;
top0 += top[0]->offset(1);
}
if (top.size()>1)
{
top[1]->Reshape(aboxes.size(), 1,1,1);
Dtype *top1 = top[1]->mutable_cpu_data();
for (int i = 0; i < aboxes.size(); ++i)
{
top1[0] = aboxes[i].score;
top1 += top[1]->offset(1);
}
}
}
#ifdef CPU_ONLY
STUB_GPU(RPNLayer);
#endif
INSTANTIATE_CLASS(RPNLayer);
REGISTER_LAYER_CLASS(RPN);
} // namespace caffe

（2）添加自定义层 roi_pooling_layer.hpp 把它放在 caffe/include/caffe/layers/ 目录下

[cpp] view plaincopy

#ifndef CAFFE_ROI_POOLING_LAYER_HPP_
#define CAFFE_ROI_POOLING_LAYER_HPP_
#include <vector>
#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
namespace caffe {
/**
* @brief Perform max pooling on regions of interest specified by input, takes
* as input N feature maps and a list of R regions of interest.
*
* ROIPoolingLayer takes 2 inputs and produces 1 output. bottom[0] is
* [N x C x H x W] feature maps on which pooling is performed. bottom[1] is
* [R x 5] containing a list R ROI tuples with batch index and coordinates of
* regions of interest. Each row in bottom[1] is a ROI tuple in format
* [batch_index x1 y1 x2 y2], where batch_index corresponds to the index of
* instance in the first input and x1 y1 x2 y2 are 0-indexed coordinates
* of ROI rectangle (including its boundaries).
*
* For each of the R ROIs, max-pooling is performed over pooled_h x pooled_w
* output bins (specified in roi_pooling_param). The pooling bin sizes are
* adaptively set such that they tile ROI rectangle in the indexed feature
* map. The pooling region of vertical bin ph in [0, pooled_h) is computed as
*
* start_ph (included) = y1 + floor(ph * (y2 - y1 + 1) / pooled_h)
* end_ph (excluded) = y1 + ceil((ph + 1) * (y2 - y1 + 1) / pooled_h)
*
* and similar horizontal bins.
*
* @param param provides ROIPoolingParameter roi_pooling_param,
* with ROIPoolingLayer options:
* - pooled_h. The pooled output height.
* - pooled_w. The pooled output width
* - spatial_scale. Multiplicative spatial scale factor to translate ROI
* coordinates from their input scale to the scale used when pooling.
*
* Fast R-CNN
* Written by Ross Girshick
*/
template <typename Dtype>
class ROIPoolingLayer : public Layer<Dtype> {
public:
explicit ROIPoolingLayer(const LayerParameter& param)
: Layer<Dtype>(param) {}
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual inline const char* type() const { return "ROIPooling"; }
virtual inline int MinBottomBlobs() const { return 2; }
virtual inline int MaxBottomBlobs() const { return 2; }
virtual inline int MinTopBlobs() const { return 1; }
virtual inline int MaxTopBlobs() const { return 1; }
protected:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
int channels_;
int height_;
int width_;
int pooled_height_;
int pooled_width_;
Dtype spatial_scale_;
Blob<int> max_idx_;
};
} // namespace caffe
#endif // CAFFE_ROI_POOLING_LAYER_HPP_

然后是源文件 roi_pooling_layer.cpp 以及cuda版的roi_pooling_layer.cu 放在 caffe/src/caffe/layers/ 目录下

[cpp] view plaincopy

#include <algorithm>
#include <cfloat>
#include <vector>
#include "caffe/layers/roi_pooling_layer.hpp"
using std::max;
using std::min;
using std::floor;
using std::ceil;
namespace caffe {
template <typename Dtype>
void ROIPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
ROIPoolingParameter roi_pool_param = this->layer_param_.roi_pooling_param();
CHECK_GT(roi_pool_param.pooled_h(), 0)
<< "pooled_h must be > 0";
CHECK_GT(roi_pool_param.pooled_w(), 0)
<< "pooled_w must be > 0";
pooled_height_ = roi_pool_param.pooled_h();
pooled_width_ = roi_pool_param.pooled_w();
spatial_scale_ = roi_pool_param.spatial_scale();
LOG(INFO) << "Spatial scale: " << spatial_scale_;
}
template <typename Dtype>
void ROIPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
channels_ = bottom[0]->channels();
height_ = bottom[0]->height();
width_ = bottom[0]->width();
top[0]->Reshape(bottom[1]->num(), channels_, pooled_height_,
pooled_width_);
max_idx_.Reshape(bottom[1]->num(), channels_, pooled_height_,
pooled_width_);
}
template <typename Dtype>
void ROIPoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->cpu_data();
const Dtype* bottom_rois = bottom[1]->cpu_data();
// Number of ROIs
int num_rois = bottom[1]->num();
int batch_size = bottom[0]->num();
int top_count = top[0]->count();
Dtype* top_data = top[0]->mutable_cpu_data();
caffe_set(top_count, Dtype(-FLT_MAX), top_data);
int* argmax_data = max_idx_.mutable_cpu_data();
caffe_set(top_count, -1, argmax_data);
// For each ROI R = [batch_index x1 y1 x2 y2]: max pool over R
for (int n = 0; n < num_rois; ++n) {
int roi_batch_ind = bottom_rois[0];
int roi_start_w = round(bottom_rois[1] * spatial_scale_);
int roi_start_h = round(bottom_rois[2] * spatial_scale_);
int roi_end_w = round(bottom_rois[3] * spatial_scale_);
int roi_end_h = round(bottom_rois[4] * spatial_scale_);
CHECK_GE(roi_batch_ind, 0);
CHECK_LT(roi_batch_ind, batch_size);
int roi_height = max(roi_end_h - roi_start_h + 1, 1);
int roi_width = max(roi_end_w - roi_start_w + 1, 1);
const Dtype bin_size_h = static_cast<Dtype>(roi_height)
/ static_cast<Dtype>(pooled_height_);
const Dtype bin_size_w = static_cast<Dtype>(roi_width)
/ static_cast<Dtype>(pooled_width_);
const Dtype* batch_data = bottom_data + bottom[0]->offset(roi_batch_ind);
for (int c = 0; c < channels_; ++c) {
for (int ph = 0; ph < pooled_height_; ++ph) {
for (int pw = 0; pw < pooled_width_; ++pw) {
// Compute pooling region for this output unit:
// start (included) = floor(ph * roi_height / pooled_height_)
// end (excluded) = ceil((ph + 1) * roi_height / pooled_height_)
int hstart = static_cast<int>(floor(static_cast<Dtype>(ph)
* bin_size_h));
int wstart = static_cast<int>(floor(static_cast<Dtype>(pw)
* bin_size_w));
int hend = static_cast<int>(ceil(static_cast<Dtype>(ph + 1)
* bin_size_h));
int wend = static_cast<int>(ceil(static_cast<Dtype>(pw + 1)
* bin_size_w));
hstart = min(max(hstart + roi_start_h, 0), height_);
hend = min(max(hend + roi_start_h, 0), height_);
wstart = min(max(wstart + roi_start_w, 0), width_);
wend = min(max(wend + roi_start_w, 0), width_);
bool is_empty = (hend <= hstart) || (wend <= wstart);
const int pool_index = ph * pooled_width_ + pw;
if (is_empty) {
top_data[pool_index] = 0;
argmax_data[pool_index] = -1;
}
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
const int index = h * width_ + w;
if (batch_data[index] > top_data[pool_index]) {
top_data[pool_index] = batch_data[index];
argmax_data[pool_index] = index;
}
}
}
}
}
// Increment all data pointers by one channel
batch_data += bottom[0]->offset(0, 1);
top_data += top[0]->offset(0, 1);
argmax_data += max_idx_.offset(0, 1);
}
// Increment ROI data pointer
bottom_rois += bottom[1]->offset(1);
}
}
template <typename Dtype>
void ROIPoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
if (propagate_down[1]) {
LOG(FATAL) << this->type()
<< " Layer cannot backpropagate to roi inputs.";
}
if (!propagate_down[0]) {
return;
}
const Dtype* bottom_rois = bottom[1]->cpu_data();
const Dtype* top_diff = top[0]->cpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
caffe_set(bottom[0]->count(), Dtype(0.), bottom_diff);
const int* argmax_data = max_idx_.cpu_data();
const int num_rois = top[0]->num();
// Accumulate gradient over all ROIs
for (int roi_n = 0; roi_n < num_rois; ++roi_n) {
int roi_batch_ind = bottom_rois[roi_n * 5];
// Accumulate gradients over each bin in this ROI
for (int c = 0; c < channels_; ++c) {
for (int ph = 0; ph < pooled_height_; ++ph) {
for (int pw = 0; pw < pooled_width_; ++pw) {
int offset_top = ((roi_n * channels_ + c) * pooled_height_ + ph)
* pooled_width_ + pw;
int argmax_index = argmax_data[offset_top];
if (argmax_index >= 0) {
int offset_bottom = (roi_batch_ind * channels_ + c) * height_
* width_ + argmax_index;
bottom_diff[offset_bottom] += top_diff[offset_top];
}
}
}
}
}
}
#ifdef CPU_ONLY
STUB_GPU(ROIPoolingLayer);
#endif
INSTANTIATE_CLASS(ROIPoolingLayer);
REGISTER_LAYER_CLASS(ROIPooling);
} // namespace caffe

[cpp] view plaincopy

#include <algorithm>
#include <cfloat>
#include <vector>
#include "caffe/layers/roi_pooling_layer.hpp"
using std::max;
using std::min;
namespace caffe {
template <typename Dtype>
__global__ void ROIPoolForward(const int nthreads, const Dtype* bottom_data,
const Dtype spatial_scale, const int channels, const int height,
const int width, const int pooled_height, const int pooled_width,
const Dtype* bottom_rois, Dtype* top_data, int* argmax_data) {
CUDA_KERNEL_LOOP(index, nthreads) {
// (n, c, ph, pw) is an element in the pooled output
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
int c = (index / pooled_width / pooled_height) % channels;
int n = index / pooled_width / pooled_height / channels;
bottom_rois += n * 5;
int roi_batch_ind = bottom_rois[0];
int roi_start_w = round(bottom_rois[1] * spatial_scale);
int roi_start_h = round(bottom_rois[2] * spatial_scale);
int roi_end_w = round(bottom_rois[3] * spatial_scale);
int roi_end_h = round(bottom_rois[4] * spatial_scale);
// Force malformed ROIs to be 1x1
int roi_width = max(roi_end_w - roi_start_w + 1, 1);
int roi_height = max(roi_end_h - roi_start_h + 1, 1);
Dtype bin_size_h = static_cast<Dtype>(roi_height)
/ static_cast<Dtype>(pooled_height);
Dtype bin_size_w = static_cast<Dtype>(roi_width)
/ static_cast<Dtype>(pooled_width);
int hstart = static_cast<int>(floor(static_cast<Dtype>(ph)
* bin_size_h));
int wstart = static_cast<int>(floor(static_cast<Dtype>(pw)
* bin_size_w));
int hend = static_cast<int>(ceil(static_cast<Dtype>(ph + 1)
* bin_size_h));
int wend = static_cast<int>(ceil(static_cast<Dtype>(pw + 1)
* bin_size_w));
// Add roi offsets and clip to input boundaries
hstart = min(max(hstart + roi_start_h, 0), height);
hend = min(max(hend + roi_start_h, 0), height);
wstart = min(max(wstart + roi_start_w, 0), width);
wend = min(max(wend + roi_start_w, 0), width);
bool is_empty = (hend <= hstart) || (wend <= wstart);
// Define an empty pooling region to be zero
Dtype maxval = is_empty ? 0 : -FLT_MAX;
// If nothing is pooled, argmax = -1 causes nothing to be backprop'd
int maxidx = -1;
bottom_data += (roi_batch_ind * channels + c) * height * width;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
int bottom_index = h * width + w;
if (bottom_data[bottom_index] > maxval) {
maxval = bottom_data[bottom_index];
maxidx = bottom_index;
}
}
}
top_data[index] = maxval;
argmax_data[index] = maxidx;
}
}
template <typename Dtype>
void ROIPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
const Dtype* bottom_rois = bottom[1]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
int* argmax_data = max_idx_.mutable_gpu_data();
int count = top[0]->count();
// NOLINT_NEXT_LINE(whitespace/operators)
ROIPoolForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, bottom_data, spatial_scale_, channels_, height_, width_,
pooled_height_, pooled_width_, bottom_rois, top_data, argmax_data);
CUDA_POST_KERNEL_CHECK;
}
template <typename Dtype>
__global__ void ROIPoolBackward(const int nthreads, const Dtype* top_diff,
const int* argmax_data, const int num_rois, const Dtype spatial_scale,
const int channels, const int height, const int width,
const int pooled_height, const int pooled_width, Dtype* bottom_diff,
const Dtype* bottom_rois) {
CUDA_KERNEL_LOOP(index, nthreads) {
// (n, c, h, w) coords in bottom data
int w = index % width;
int h = (index / width) % height;
int c = (index / width / height) % channels;
int n = index / width / height / channels;
Dtype gradient = 0;
// Accumulate gradient over all ROIs that pooled this element
for (int roi_n = 0; roi_n < num_rois; ++roi_n) {
const Dtype* offset_bottom_rois = bottom_rois + roi_n * 5;
int roi_batch_ind = offset_bottom_rois[0];
// Skip if ROI's batch index doesn't match n
if (n != roi_batch_ind) {
continue;
}
int roi_start_w = round(offset_bottom_rois[1] * spatial_scale);
int roi_start_h = round(offset_bottom_rois[2] * spatial_scale);
int roi_end_w = round(offset_bottom_rois[3] * spatial_scale);
int roi_end_h = round(offset_bottom_rois[4] * spatial_scale);
// Skip if ROI doesn't include (h, w)
const bool in_roi = (w >= roi_start_w && w <= roi_end_w &&
h >= roi_start_h && h <= roi_end_h);
if (!in_roi) {
continue;
}
int offset = (roi_n * channels + c) * pooled_height * pooled_width;
const Dtype* offset_top_diff = top_diff + offset;
const int* offset_argmax_data = argmax_data + offset;
// Compute feasible set of pooled units that could have pooled
// this bottom unit
// Force malformed ROIs to be 1x1
int roi_width = max(roi_end_w - roi_start_w + 1, 1);
int roi_height = max(roi_end_h - roi_start_h + 1, 1);
Dtype bin_size_h = static_cast<Dtype>(roi_height)
/ static_cast<Dtype>(pooled_height);
Dtype bin_size_w = static_cast<Dtype>(roi_width)
/ static_cast<Dtype>(pooled_width);
int phstart = floor(static_cast<Dtype>(h - roi_start_h) / bin_size_h);
int phend = ceil(static_cast<Dtype>(h - roi_start_h + 1) / bin_size_h);
int pwstart = floor(static_cast<Dtype>(w - roi_start_w) / bin_size_w);
int pwend = ceil(static_cast<Dtype>(w - roi_start_w + 1) / bin_size_w);
phstart = min(max(phstart, 0), pooled_height);
phend = min(max(phend, 0), pooled_height);
pwstart = min(max(pwstart, 0), pooled_width);
pwend = min(max(pwend, 0), pooled_width);
for (int ph = phstart; ph < phend; ++ph) {
for (int pw = pwstart; pw < pwend; ++pw) {
if (offset_argmax_data[ph * pooled_width + pw] == (h * width + w)) {
gradient += offset_top_diff[ph * pooled_width + pw];
}
}
}
}
bottom_diff[index] = gradient;
}
}
template <typename Dtype>
void ROIPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
if (!propagate_down[0]) {
return;
}
const Dtype* bottom_rois = bottom[1]->gpu_data();
const Dtype* top_diff = top[0]->gpu_diff();
Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
const int count = bottom[0]->count();
caffe_gpu_set(count, Dtype(0.), bottom_diff);
const int* argmax_data = max_idx_.gpu_data();
// NOLINT_NEXT_LINE(whitespace/operators)
ROIPoolBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
count, top_diff, argmax_data, top[0]->num(), spatial_scale_, channels_,
height_, width_, pooled_height_, pooled_width_, bottom_diff, bottom_rois);
CUDA_POST_KERNEL_CHECK;
}
INSTANTIATE_LAYER_GPU_FUNCS(ROIPoolingLayer);
} // namespace caffe

（3）好了，代码添加完毕，现在在caffe/src/caffe/proto/caffe.proto 中声明这两个类

根据你自己的可用ID 在message Layer中添加这两个类，我的已经添加了，大概是这样的，千万记住大小写！

[cpp] view plaincopy

// NOTE
// Update the next available ID when you add a new LayerParameter field.
// LayerParameter next available layer-specific ID: 152 (last added: rpn_param roi_pooling_param)

[cpp] view plaincopy

optional RPNParameter rpn_param = 150; //
optional ROIPoolingParameter roi_pooling_param = 151; // roi pooling Faster-Rcnn

这里写好后，因为这两个层都有内置的参数，还得在这个文件的最末尾，定义具体的参数

[cpp] view plaincopy

message ROIPoolingParameter {
optional uint32 pooled_h = 1 [default = 0];
optional uint32 pooled_w = 2 [default = 0];
optional float spatial_scale = 3 [default = 1];
}
message RPNParameter {
optional uint32 feat_stride = 1;
optional uint32 basesize = 2;
repeated uint32 scale = 3;
repeated float ratio = 4;
optional uint32 boxminsize =5;
optional uint32 per_nms_topn = 9;
optional uint32 post_nms_topn = 11;
optional float nms_thresh = 8;
}

（4）因为自定义层使用了RPN层，为了以后程序中各处都能使用该层，所以得在common.hpp和common.cpp文件的最末尾，添加对应的代码，注意这里的namespace RPN是和namespace caffe同一级的

头文件common.hpp里添加

[cpp] view plaincopy

namespace RPN{
struct abox
{
float x1;
float y1;
float x2;
float y2;
float score;
bool operator <(const abox&tmp) const{
return score < tmp.score;
}
};
void nms(std::vector<abox>& input_boxes,float nms_thresh);
cv::Mat bbox_tranform_inv(cv::Mat, cv::Mat);
} // namespace RPN

源文件common.cpp里，为了防止说找不到cv::Mat类型的错误，添加opencv头文件

#include<opencv2/opencv.hpp>

using namespace cv;

[cpp] view plaincopy

namespace RPN{
cv::Mat bbox_tranform_inv(cv::Mat local_anchors, cv::Mat boxs_delta){
cv::Mat pre_box(local_anchors.rows, local_anchors.cols, CV_32FC1);
for (int i = 0; i < local_anchors.rows; i++)
{
double pred_ctr_x, pred_ctr_y, src_ctr_x, src_ctr_y;
double dst_ctr_x, dst_ctr_y, dst_scl_x, dst_scl_y;
double src_w, src_h, pred_w, pred_h;
src_w = local_anchors.at<float>(i, 2) - local_anchors.at<float>(i, 0) + 1;
src_h = local_anchors.at<float>(i, 3) - local_anchors.at<float>(i, 1) + 1;
src_ctr_x = local_anchors.at<float>(i, 0) + 0.5 * src_w;
src_ctr_y = local_anchors.at<float>(i, 1) + 0.5 * src_h;
dst_ctr_x = boxs_delta.at<float>(i, 0);
dst_ctr_y = boxs_delta.at<float>(i, 1);
dst_scl_x = boxs_delta.at<float>(i, 2);
dst_scl_y = boxs_delta.at<float>(i, 3);
pred_ctr_x = dst_ctr_x*src_w + src_ctr_x;
pred_ctr_y = dst_ctr_y*src_h + src_ctr_y;
pred_w = exp(dst_scl_x) * src_w;
pred_h = exp(dst_scl_y) * src_h;
pre_box.at<float>(i, 0) = pred_ctr_x - 0.5*pred_w;
pre_box.at<float>(i, 1) = pred_ctr_y - 0.5*pred_h;
pre_box.at<float>(i, 2) = pred_ctr_x + 0.5*pred_w;
pre_box.at<float>(i, 3) = pred_ctr_y + 0.5*pred_h;
}
return pre_box;
}
void nms(std::vector<abox> &input_boxes, float nms_thresh){
std::vector<float>vArea(input_boxes.size());
for (int i = 0; i < input_boxes.size(); ++i)
{
vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1)
* (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 1);
}
for (int i = 0; i < input_boxes.size(); ++i)
{
for (int j = i + 1; j < input_boxes.size();)
{
float xx1 = std::max(input_boxes[i].x1, input_boxes[j].x1);
float yy1 = std::max(input_boxes[i].y1, input_boxes[j].y1);
float xx2 = std::min(input_boxes[i].x2, input_boxes[j].x2);
float yy2 = std::min(input_boxes[i].y2, input_boxes[j].y2);
float w = std::max(float(0), xx2 - xx1 + 1);
float h = std::max(float(0), yy2 - yy1 + 1);
float inter = w * h;
float ovr = inter / (vArea[i] + vArea[j] - inter);
if (ovr >= nms_thresh)
{
input_boxes.erase(input_boxes.begin() + j);
vArea.erase(vArea.begin() + j);
}
else
{
j++;
}
}
}
}
}

（5）好了，配置弄完了，回到caffe根目录下，

make clean

make all -j

开始编译吧！

可能会出现什么找不到pb.h文件什么的，那就继续执行 make -j5 可能是因为编译的线程太多导致先后顺序什么的。我也是猜的，反正我是这么解决的。

（6）环境已经配置好了，现在我们再加个类，用来对图片进行检测吧！编写头文件ObjectDetector.hpp

[cpp] view plaincopy

#ifndef OBJECTDETECTOR_H
#define OBJECTDETECTOR_H
#define INPUT_SIZE_NARROW 600
#define INPUT_SIZE_LONG 1000
#include <string>
#include <caffe/net.hpp>
#include <caffe/common.hpp>
#include <opencv2/core/core.hpp>
#include <iostream>
#include <memory>
#include <map>
using namespace std;
class ObjectDetector
{
public:
ObjectDetector(const std::string &model_file, const std::string &weights_file); //构造函数
//对一张图片，进行检测，将结果保存进map数据结构里,分别表示每个类别对应的目标框，如果需要分数信息，则计算分数
map<int,vector<cv::Rect> > detect(const cv::Mat& image, map<int,vector<float> >* score=NULL);
private:
boost::shared_ptr< caffe::Net<float> > net_;
int class_num_; //类别数+1 ,官方给的demo 是20+1类
};
#endif

源文件ObjectDetector.cpp

[cpp] view plaincopy

#include "ObjectDetector.hpp"
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <vector>
#include <fstream>
using std::string;
using std::vector;
using namespace caffe;
using std::max;
using std::min;
ObjectDetector::ObjectDetector(const std::string &model_file,const std::string &weights_file){
#ifdef CPU_ONLY
Caffe::set_mode(Caffe::CPU);
#else
Caffe::set_mode(Caffe::GPU);
#endif
net_.reset(new Net<float>(model_file, TEST));
net_->CopyTrainedLayersFrom(weights_file);
this->class_num_ = net_->blob_by_name("cls_prob")->channels(); //求得类别数+1
}
//对一张图片，进行检测，将结果保存进map数据结构里,分别表示每个类别对应的目标框，如果需要分数信息，则计算分数
map<int,vector<cv::Rect> > ObjectDetector::detect(const cv::Mat& image,map<int,vector<float> >* objectScore){
if(objectScore!=NULL) //如果需要保存置信度
objectScore->clear();
float CONF_THRESH = 0.8; //置信度阈值
float NMS_THRESH = 0.3; //非极大值抑制阈值
int max_side = max(image.rows, image.cols); //分别求出图片宽和高的较大者
int min_side = min(image.rows, image.cols);
float max_side_scale = float(max_side) / float(INPUT_SIZE_LONG); //分别求出缩放因子
float min_side_scale = float(min_side) / float(INPUT_SIZE_NARROW);
float max_scale = max(max_side_scale, min_side_scale);
float img_scale = float(1) / max_scale;
int height = int(image.rows * img_scale);
int width = int(image.cols * img_scale);
int num_out;
cv::Mat cv_resized;
image.convertTo(cv_resized, CV_32FC3);
cv::resize(cv_resized, cv_resized, cv::Size(width, height));
cv::Mat mean(height, width, cv_resized.type(), cv::Scalar(102.9801, 115.9465, 122.7717));
cv::Mat normalized;
subtract(cv_resized, mean, normalized);
float im_info[3];
im_info[0] = height;
im_info[1] = width;
im_info[2] = img_scale;
shared_ptr<Blob<float> > input_layer = net_->blob_by_name("data");
input_layer->Reshape(1, normalized.channels(), height, width);
net_->Reshape();
float* input_data = input_layer->mutable_cpu_data();
vector<cv::Mat> input_channels;
for (int i = 0; i < input_layer->channels(); ++i) {
cv::Mat channel(height, width, CV_32FC1, input_data);
input_channels.push_back(channel);
input_data += height * width;
}
cv::split(normalized, input_channels);
net_->blob_by_name("im_info")->set_cpu_data(im_info);
net_->Forward(); //进行网络前向传播
int num = net_->blob_by_name("rois")->num(); //产生的 ROI 个数,比如为 13949个ROI
const float *rois_data = net_->blob_by_name("rois")->cpu_data(); //维度比如为：13949*5*1*1
int num1 = net_->blob_by_name("bbox_pred")->num(); //预测的矩形框维度为 13949*84
cv::Mat rois_box(num, 4, CV_32FC1);
for (int i = 0; i < num; ++i)
{
rois_box.at<float>(i, 0) = rois_data[i * 5 + 1] / img_scale;
rois_box.at<float>(i, 1) = rois_data[i * 5 + 2] / img_scale;
rois_box.at<float>(i, 2) = rois_data[i * 5 + 3] / img_scale;
rois_box.at<float>(i, 3) = rois_data[i * 5 + 4] / img_scale;
}
shared_ptr<Blob<float> > bbox_delt_data = net_->blob_by_name("bbox_pred"); // 13949*84
shared_ptr<Blob<float> > score = net_->blob_by_name("cls_prob"); // 3949*21
map<int,vector<cv::Rect> > label_objs; //每个类别，对应的检测目标框
for (int i = 1; i < class_num_; ++i){ //对每个类，进行遍历
cv::Mat bbox_delt(num, 4, CV_32FC1);
for (int j = 0; j < num; ++j){
bbox_delt.at<float>(j, 0) = bbox_delt_data->data_at(j, i * 4 + 0, 0, 0);
bbox_delt.at<float>(j, 1) = bbox_delt_data->data_at(j, i * 4 + 1, 0, 0);
bbox_delt.at<float>(j, 2) = bbox_delt_data->data_at(j, i * 4 + 2, 0, 0);
bbox_delt.at<float>(j, 3) = bbox_delt_data->data_at(j, i * 4 + 3, 0, 0);
}
cv::Mat box_class = RPN::bbox_tranform_inv(rois_box, bbox_delt);
vector<RPN::abox> aboxes; //对于类别i，检测出的矩形框保存在这
for (int j = 0; j < box_class.rows; ++j){
if (box_class.at<float>(j, 0) < 0) box_class.at<float>(j, 0) = 0;
if (box_class.at<float>(j, 0) > (image.cols - 1)) box_class.at<float>(j, 0) = image.cols - 1;
if (box_class.at<float>(j, 2) < 0) box_class.at<float>(j, 2) = 0;
if (box_class.at<float>(j, 2) > (image.cols - 1)) box_class.at<float>(j, 2) = image.cols - 1;
if (box_class.at<float>(j, 1) < 0) box_class.at<float>(j, 1) = 0;
if (box_class.at<float>(j, 1) > (image.rows - 1)) box_class.at<float>(j, 1) = image.rows - 1;
if (box_class.at<float>(j, 3) < 0) box_class.at<float>(j, 3) = 0;
if (box_class.at<float>(j, 3) > (image.rows - 1)) box_class.at<float>(j, 3) = image.rows - 1;
RPN::abox tmp;
tmp.x1 = box_class.at<float>(j, 0);
tmp.y1 = box_class.at<float>(j, 1);
tmp.x2 = box_class.at<float>(j, 2);
tmp.y2 = box_class.at<float>(j, 3);
tmp.score = score->data_at(j, i, 0, 0);
aboxes.push_back(tmp);
}
std::sort(aboxes.rbegin(), aboxes.rend());
RPN::nms(aboxes, NMS_THRESH); //与非极大值抑制消除对于的矩形框
for (int k = 0; k < aboxes.size();){
if (aboxes[k].score < CONF_THRESH)
aboxes.erase(aboxes.begin() + k);
else
k++;
}
//################ 将类别i的所有检测框，保存
vector<cv::Rect> rect(aboxes.size()); //对于类别i，检测出的矩形框
for(int ii=0;ii<aboxes.size();++ii)
rect[ii]=cv::Rect(cv::Point(aboxes[ii].x1,aboxes[ii].y1),cv::Point(aboxes[ii].x2,aboxes[ii].y2));
label_objs[i]=rect;
//################ 将类别i的所有检测框的打分，保存
if(objectScore!=NULL){ //################ 将类别i的所有检测框的打分，保存
vector<float> tmp(aboxes.size()); //对于类别i，检测出的矩形框的得分
for(int ii=0;ii<aboxes.size();++ii)
tmp[ii]=aboxes[ii].score;
objectScore->insert(pair<int,vector<float> >(i,tmp));
}
}
return label_objs;
}

这里的代码，是在参考博客中的代码，我改了下，加了自己的需求。这里的函数返回的是一个map对象，每一个键（类别label），对应一个矩形框向量。比如，一个20类检测任务，而一张图片里有3个人（标签是1），和2辆车（标签是5），那函数会返回一个map，其中有两个键值对，键1对应的值是一个3维的矩形框向量，分别代表着3个人的矩形框；键5对应的值是一个2维的矩形框向量，分别代表的是2辆车的矩形框。同时，函数还接受一个可选参数，可以返回每个矩形框各自对应的置信度。

Ok，现在我们写个主函数，测试下效果吧，我们建个文件夹，首先把网络描述文件test.prototxt拷贝过来，这里我用的是VGG16的，end2end的网络，路径是py-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_end2end/test.prototxt，Ok，拷贝过来，因为我们不需要python层了，那我们打开这个文件，定位到 Python层，

[cpp] view plaincopy

layer {
name: 'proposal'
type: 'Python'
bottom: 'rpn_cls_prob_reshape'
bottom: 'rpn_bbox_pred'
bottom: 'im_info'
top: 'rois'
python_param {
module: 'rpn.proposal_layer'
layer: 'ProposalLayer'
param_str: "'feat_stride': 16"
}
}

把它修改为

[cpp] view plaincopy

layer {
name: "proposal"
type: "RPN"
bottom: "rpn_cls_prob_reshape"
bottom: "rpn_bbox_pred"
bottom: "im_info"
top: "rois"
rpn_param {
feat_stride : 16
basesize : 16
scale : 8
scale : 16
scale : 32
ratio : 0.5
ratio : 1
ratio : 2
boxminsize :16
per_nms_topn : 0;
post_nms_topn : 0;
nms_thresh : 0.3
}
}

是的，这里的一系列参数，可以自己设置的，大家可以尝试下

然后，我们需要一个已经训练好的检测caffemodel，这里我直接拿示例的20类demo的caffemodel，也把它拷贝到我们的文件夹下，万事俱备，只欠东风了！赶紧编写个主函数进行测试吧，我的示例如下：

[cpp] view plaincopy

#include "ObjectDetector.hpp"
#include<opencv2/opencv.hpp>
#include<iostream>
#include<sstream>
using namespace cv;
using namespace std;
string num2str(float i){
stringstream ss;
ss<<i;
return ss.str();
}
int main(int argc,char **argv){
::google::InitGoogleLogging(argv[0]);
#ifdef CPU_ONLY
cout<<"Use CPU\n";
#else
cout<<"Use GPU\n";
#endif
ObjectDetector detect("test.prototxt","1.caffemodel");
Mat img=imread("1.jpg");
map<int,vector<float> > score;
map<int,vector<Rect> > label_objs=detect.detect(img,&score); //目标检测,同时保存每个框的置信度
for(map<int,vector<Rect> >::iterator it=label_objs.begin();it!=label_objs.end();it++){
int label=it->first; //标签
vector<Rect> rects=it->second; //检测框
for(int j=0;j<rects.size();j++){
rectangle(img,rects[j],Scalar(0,0,255),2); //画出矩形框
string txt=num2str(label)+" : "+num2str(score[label][j]);
putText(img,txt,Point(rects[j].x,rects[j].y),CV_FONT_HERSHEY_SIMPLEX,0.5,Scalar(0,255,0)); //标记类别：置信度
}
}
imshow("", img);
waitKey();
return 0;
}

好了，这里网络描述文件是 test.prototxt，调用的是caffemodel是官方示例的model，我这为了简单，改名1.caffemodel了，对图片1.jpg进行测试，现在编译main.cpp 文件，命令如下：

[cpp] view plaincopy

app.bin: main.cpp ObjectDetector.cpp
g++ -o app.bin main.cpp ObjectDetector.cpp -I /home/*****/caffe/include/ -I /home/*****/caffe/.build_release/src/ -I /usr/local/cuda-8.0/include/ `pkg-config --libs --cflags opencv` -L /home/****/caffe/build/lib/ -lcaffe -lglog -lboost_system -lprotobuf

具体路径参照自己的就好，生成app.bin可执行文件，运行，我们对一张图片进行测试，原图如下

检测后，如下：

这里为了方便，我直接输出的标签号以及对应的置信度了。可以看出，飞机的的label为1，船的label是4，我们从python版的demo.py中可以证实这点：

[cpp] view plaincopy

CLASSES = ('__background__',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')

OK，大功告成啦~~~

纯C++版的Faster-Rcnn（通过caffe自定义RPN层实现）相关推荐

目标检测经典论文——Faster R-CNN论文翻译（纯中文版）：Faster R-CNN：通过Region Proposal网络实现实时目标检测
目标检测经典论文翻译汇总:[翻译汇总] 翻译pdf文件下载:[下载地址] 此版为纯中文版,中英文对照版请稳步:[Faster R-CNN中英文对照版] Faster R-CNN:通过Region Pr ...
记录配置faster rcnn（caffe）CPU版本遇到的问题
运行Faster-Rcnn代码与Faster-Rcnn一样Faster-Rcnn官方也是采用caffe作为框架. 首先将项目克隆到本地(需要挂代理) Make sure to clone with ...
faster rcnn windows 下c++版本
faster rcnn windows 下c++版本参考了http://www.cnblogs.com/louyihang-loves-baiyan/p/5485955.html,和http://b ...
（目标检测）Faster R-CNN 论文解读+复现
Faster R-CNN xyang 声明:本篇文章借用了他人理解,如有侵权,请联系,另如需转载,请注明出处关于最新最全的目标检测论文,可以查看awesome-object-detection &l ...
Faster R-CNN改进篇（一）： ION ● HyperNet ● MS CNN
版权声明:本文为博主原创文章,未经博主允许不得转载. https://blog.csdn.net/linolzhang/article/details/74159463 一. 源起于Faster 深度 ...
Faster R-CNN论文及源码解读
R-CNN是目标检测领域中十分经典的方法,相比于传统的手工特征,R-CNN将卷积神经网络引入,用于提取深度特征,后接一个分类器判决搜索区域是否包含目标及其置信度,取得了较为准确的检测结果.Fast R ...
最详细的Faster RCNN论文笔记
个人博客:http://www.chenjianqu.com/ 原文链接:http://www.chenjianqu.com/show-76.html 论文:Shaoqing Ren, Kaiming ...
目标检测 -- R-CNN，Fast R-CNN，Faster R-CNN
R-CNN,Fast R-CNN,Faster R-CNN这些是深度学习目标检测的鼻祖.看各种博客分析,东看看西看看,不系统.这里准备系统的记录一下深度学习目标检测的发展史.这里大部分摘录其他博客.参 ...
目标检测——Faster R-CNN论文阅读
论文阅读--Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks 文章目录论文阅读--Faste ...

纯C++版的Faster-Rcnn（通过caffe自定义RPN层实现）

纯C++版的Faster-Rcnn（通过caffe自定义RPN层实现）相关推荐

最新文章

热门文章